Computer-implemented systems utilizing sensor networks for sensing temperature and motion environmental parameters; and methods of use thereof

ABSTRACT

Computer-implemented systems utilizing sensor networks for sensing temperature and motion environmental parameters, and performing at least operations of electronically establishing, based on pattern recognition criteria, correspondence of a plurality of representative features a plurality of characteristics of an occurrence, where a first instance of the occurrence occurred within a first time period of a plurality of time periods; electronically discovering, based on the correspondence, a second instance of the occurrence in an environment during a second time period of the plurality of time periods; and electronically causing, based on the discovery of the second instance of the occurrence, a change in the environment via an electronically-controlled device.

RELATED APPLICATIONS

The present application is related to, claims the earliest availableeffective filing date(s) from the following listed application(s) (the“Related Application(s)”) (e.g., claims earliest available prioritydates for other than provisional patent applications or claims benefitsunder 35 USC §119(e) for provisional patent applications), andincorporates by reference in its entirety all subject matter of thefollowing listed application(s); the present application also claims theearliest available effective filing date(s) from, and also incorporatesby reference in its entirety all subject matter of any and all parent,grandparent, great-grandparent, etc. applications of the RelatedApplication(s)”.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation of U.S. patent application Ser.No. 10/909,200, filed entitled DISCOVERY OF OCCURRENCE-DATA, namingEdward K. Y. Jung and Clarence T. Tegreene as inventors, now issued U.S.Pat. No. 9,261,383.

For purposes of the USPTO extra-statutory requirements, U.S. patentapplication Ser. No. 10/909,200, constitutes a continuation-in-part ofU.S. patent application Ser. No. 10/903,692, filed Jul. 30, 2004,entitled AGGREGATION AND RETRIEVAL OF NETWORK SENSOR DATA, naming EdwardK. Y. Jung and Clarence T. Tegreene as inventors, now issued U.S. Pat.No. 7,457,834.

For purposes of the USPTO extra-statutory requirements, U.S. patentapplication Ser. No. 10/909,200, constitutes a continuation-in-part ofU.S. patent application Ser. No. 10/903,652, filed Jul. 30, 2004,entitled AGGREGATION AND RETRIEVAL OF NETWORK SENSOR DATA, naming EdwardK. Y. Jung and Clarence T. Tegreene as inventors, now issued U.S. Pat.No. 7,536,388.

BACKGROUND

The present era of computing has introduced an array of small devicesthat perform a variety of specific functions. Cellular phones, pagersand portable digital assistants are common examples of these. Astechnology progresses, however, devices will continue to become smallerand more specialized. One class of small device that is beginning toemerge is a tiny, sensor, sometimes known as a “mote” that is oftenimplemented in a networked configuration.

Networked sensor nodes, sometimes referred to as sensor devices, areundergoing significant advances in structure and low power technology.In some applications, sensor nodes may utilize micro-electromechanicalsystems, or MEMS, technology. Sensor nodes may include more than onecomponent, such as an embedded processor, digital storage, power source,a transceiver, and an array of sensors, environmental detectors, and/oractuators. In some cases, sensor nodes may rely on small batteries,solar-powered cell, or ambient energy for power, and run for longperiods of time without maintenance.

Communication characteristics of nodes may be determined by physicaldesign characteristics and intended use scenarios or both. In someapplications, sensor nodes may act as a data source, and it may alsoforward data from other sensors that are out of range of a centralstation.

The practical applications of such mini-devices range from environmentalmonitoring to micro-robots capable of performing microscopic scaletasks. While functionality of an individual sensor node may be limited,a grouping of nodes working together can accomplish a range of tasks,including high level tasks. The tasks of a grouping may includeoperations such as general information gathering, security, industrialmonitoring, military reconnaissance, or biomedical monitoring.

The integration of computation, storage, communication, and physicalinteraction in silicon has shrunk some sensor nodes down to microscopicscales. The ability to create sensors and actuators with IC technologyand integrate them with computational logic has created an abundance oflow-power, tiny sensor nodes. Combining these tiny sensor nodes with lowpower wireless communication networks aids in developing economical,distributed sensors networks. The number of sensor nodes used in anetwork is increasing as their cost decreases and functionalityincreases. As a result, the sheer volume of data created by sensornetworks, particularly distributed sensor networks, is rapidlyincreasing.

SUMMARY

An embodiment provides an occurrence-data retrieval system. The systemincludes a data storage operable to store a plurality of instances ofoccurrence-data, each instance of the occurrence-data having arepresentative feature, a central computing device operable tocommunicate with the data storage, and instructions that cause acomputing device to perform steps. The steps include receive from aninput-selector an input selection corresponding to a target-occurrencehaving a representative feature, and select a pattern recognitioncriteria corresponding to the representative feature of thetarget-occurrence. The steps also include automatically search theplurality of instances of stored occurrence-data for data correlating tothe target-occurrence using the selected pattern recognition criteria,and provide an output indicative of a result of the automatic search.The input-selector may include an individual user. The patternrecognition criteria may be automatically selected in response to inputselection corresponding to the target-occurrence.

The input selection may further include a representative feature of thetarget-occurrence. The representative feature may include a time period.The representative feature may include acoustic frequency components.The representative feature may include a frequency pattern. Thefrequency pattern may include at least one selected from a groupconsisting of a recognized word, a set of words, a breaking glass, a dogbark, a door opening, an alarm, a threshold acoustic level, and avoiceprint. The representative feature may include an electromagneticpattern. The electromagnetic pattern may include at least one selectedfrom a group consisting of a visible light, an infrared light, anultraviolet light, and a radar. The recognition criteria may beautomatically selected in response to the selected representativefeature. The automatic search instruction may include using the patternrecognition criteria selected in response to the inputted representativefeature. The instruction to provide an output may include provide aninstance of the correlating occurrence-data. The correlatingoccurrence-data provided may include a segment of the correlatingoccurrence-data. The instruction to provide an output may includeprovide a degraded representation of an instance of the correlatingoccurrence-data. The instruction to provide an output may includeprovide an instance of non-correlating occurrence-data. Thenon-correlating occurrence-data provided may include a degradedrepresentation of the non-correlating occurrence-data. Theoccurrence-data may include sensor data generated by a plurality ofnetworked remote sensor devices. The instructions may include protectthe plurality of instances of occurrence-data stored in the data storagefrom unauthorized access. The data storage may include a digital datastorage device. Each instance of occurrence-data may include a datasequence, and the data sequence may include a chronological datasequence.

Another embodiment provides an occurrence-data retrieval system. Thesystem includes a computing device operable to communicate with a datastorage device. The data storage device is operable to store a pluralityof instances of occurrence-data from remote data storages. Each instanceof occurrence-data including a representative feature sensedrespectively by a device associated with the remote data storage. Thesystem also includes an information security measure that protectsinstances of occurrence-data stored in the data storage device fromunauthorized access, and instructions, which when implemented in acomputing device, cause the computing device to perform steps. The stepsinclude receive from an input-selector an input selection correspondingto a target-occurrence having a representative feature, a recipientselection, and a tendered access authorization. In response to thetendered access authorization, determine if at least one of theinput-selector and recipient have an access right. Also, automaticallyselect a pattern recognition criteria corresponding to at least onerepresentative feature of the target-occurrence, and in response to theinput selection corresponding to the target-occurrence, automaticallysearch the plurality of instances of occurrence-data stored in the datastorage device for data correlating to the target-occurrence using theselected pattern recognition criteria. If at least one of theinput-selector and recipient have an access right, provide an outputindicative of a result of the automatic search to the recipient.

The input-selector may include an individual user. The input-selectorand the recipient may be a same party. The recipient may be anindividual user. The information security measure may be associated withthe data storage device. The information security measure may include anapplication associated with the computing device. The data storagedevice may include at least one device selected from a group consistingof a local data storage device and a remote data storage device. Thedata storage device may include a portable digital data storage device.The instruction to provide an output indicative of a result may includeprovide the correlating occurrence-data to the recipient. The steps ofthe instructions may include receive a redaction selection, and a tenderof a redaction authorization, and determine if a redaction right ispossessed. In response to the redaction selection and a determinationthat a redaction right is possessed, redact an instance of the pluralityof instances of occurrence-data from the data storage device. Theredaction selection may be received from at least one of theinput-selector and the recipient. The redacted instance ofoccurrence-data may correlate to the target-occurrence representativefeature. The redacted instance of occurrence-data may not correlate tothe target-occurrence representative feature.

A further embodiment provides an occurrence-data retrieval system. Thesystem includes a computing device operable to communicate with a datastorage device. The data storage device is operable to store a pluralityof instances of occurrence-data from remote data storages. Each instanceof occurrence-data having a representative feature sensed respectivelyby a device associated with the remote data storage. The system alsoincludes an information security measure that protects instances ofoccurrence-data stored in the data storage device from unauthorizedaccess, and instructions that cause a computing device to perform steps.The steps include receive from a redaction-selector a redactionselection corresponding to a target-occurrence having a representativefeature, and a tender of a redaction authorization. In response to thetendered redaction authorization, determine if the redaction-selectorpossess a redaction right. Automatically select a pattern recognitioncriteria corresponding to the representative feature of thetarget-occurrence, and automatically search the plurality of instancesof occurrence-data stored in the data storage device for datacorrelating to the target-occurrence using the selected patternrecognition criteria. If the redaction-selector possesses a redactionright, redact an instance of the plurality of instances ofoccurrence-data from the data storage device. The redacted instance ofoccurrence-data may correlate to the target-occurrence representativefeature. The redacted instance of occurrence-data may not correlate tothe target-occurrence representative feature. The instructions mayinclude computer program instructions.

An embodiment provides a method implemented in a computing device. Themethod includes receiving an input selection from an input-selector, theinput selection corresponding to a target-occurrence having arepresentative feature, and selecting a pattern recognition criteriacorresponding to the representative feature of the target-occurrence. Inresponse to the input selection corresponding to the target-occurrence,automatically searching a plurality of instances of occurrence-datastored in a data storage device for data correlating to thetarget-occurrence representative feature using the selected patternrecognition criteria. Each instance of the occurrence-data includes arepresentative feature. Also, provide an output indicative of the searchresults. The pattern recognition criteria may be automatically selectedin response to the target-occurrence. The input selection may includeselection of a representative feature of the target-occurrence. Thepattern recognition criteria may be automatically selected in responseto the input-selector selected representative feature. The automaticallysearching step may use the pattern recognition criteria selected inresponse to the input-selector selected representative feature.

The providing an output may include providing an instance of thecorrelating occurrence-data. The provided instance of correlatingoccurrence-data may include a degraded representation of the correlatingoccurrence-data. Alternatively, the provided instance of correlatingoccurrence-data may include all data associated with the correlatingoccurrence. The provided correlating occurrence-data may include asegment of the correlating occurrence-data. The providing an output mayinclude providing an instance of non-correlating occurrence-data. Theinstance of correlating occurrence-data may include a degradedrepresentation of the non-correlating occurrence-data. Theoccurrence-data may include sensor data generated by a plurality ofnetworked sensor devices.

Another embodiment provides a method implemented in a computing device.The method includes receiving from an input-selector an input selectioncorresponding to a target-occurrence having a representative feature,and selecting a filter corresponding to the representative feature ofthe target-occurrence. Also, using the selected filter, automaticallyfiltering a plurality of instances of occurrence-data stored in a dataset for data correlating to the target-occurrence representativefeature, each instance of the occurrence-data having a representativefeature. The method includes providing an output responsive to thefiltering. The providing an output may include providing an instance ofoccurrence-data correlating to a target-occurrence representativefeature, and may include storing the instance of occurrence-datacorrelating to a target-occurrence representative feature. The providingan output may include providing an instance of occurrence-data notcorrelating to a target-occurrence representative feature, and mayinclude storing the instance of occurrence-data not correlating to atarget-occurrence representative feature.

A further embodiment provides a method. The method includes inputting aselection to a computing device corresponding to a target-occurrencehaving a representative feature, and inputting a selection to thecomputing device corresponding to a plurality of instances ofoccurrence-data obtained from remote data storages. Each instance of theoccurrence-data includes a representative feature sensed respectively bya device associated with the remote data storage. In response to theinput selection, receiving an instance of occurrence-data correlating tothe target-occurrence from the computing device. The plurality ofinstances of occurrence-data may be stored in a data storage devicelocal to the computing device. The received instance of occurrence-datamay include a feature correlating to a target-occurrence representativefeature automatically selected by the computing device. The inputselection corresponding to the target-occurrence may include selectionof a representative feature of the target-occurrence. The receivedinstance of occurrence-data may include an instance of occurrence-datahaving a feature correlating to the selected target-occurrencerepresentative feature.

An embodiment provides a method implemented in a computing device. Themethod includes receiving an input selection from an input-selector, theinput selection corresponding to a target-occurrence having arepresentative feature, a recipient selection, and a tendered accessauthorization. In response to the tendered access authorization,determining if at least one of the input-selector and the recipientpossess an access right to a plurality of instances of storedoccurrence-data protected by an information security measure. Eachinstance of occurrence-data originating from remote data storages,having a representative feature sensed respectively by a deviceassociated with the remote data storage, and respectively correlating toan occurrence. Also, automatically selecting a pattern recognitioncriteria corresponding to the representative feature of thetarget-occurrence. In response to the input selection corresponding tothe target-occurrence, automatically searching the plurality ofinstances of stored occurrence-data for data correlating to therepresentative feature of the target-occurrence using the selectedpattern recognition criteria. If at least one of the input-selector andrecipient posses an access right, providing an output indicative of aresult of the automatic search to the recipient. The occurrence-data maybe stored in a data storage device, and, the data storage device mayinclude a digital data storage device. The data storage device mayinclude a portable data storage device. The information security measuremay be associated with the data storage device, and may be associatedwith the computing device. The input-selector may include an individualuser. The recipient may be an individual user. The input-selector andthe recipient may be a same party. The providing an output indicative ofa result of the automatic search may include providing a ranking for atleast two instances of the correlating occurrence-data in a hierarchy ofthe found correlating occurrence-data.

The providing an output indicative of a result of the automatic searchmay include providing a tentative target-occurrence identifier. Themethod may include steps for receiving another input-selectioncorresponding to the tentative target-occurrence identifier, andproviding an instance of correlating occurrence-data in response to theanother input-selection.

The providing an output indicative of a result of the automatic searchmay include providing a degraded representation of an instance of thecorrelating occurrence-data. The method may include steps for receivinganother input-selection corresponding to the degraded representation,and providing correlating occurrence-data in response to the anotherinput-selection.

The method may include receiving a redaction selection and a tenderedredaction authorization, and determining that at least one of theredaction-selector and recipient possess a redaction right. In responseto the redaction selection and a determination that at least one of theredaction selector and the recipient possess a redaction right,redacting an instance of the plurality of instances of occurrence-datafrom the stored occurrence-data. The redacted instance ofoccurrence-data may correlate to the target-occurrence representativefeature. The redacted instance of occurrence-data may not correlate tothe target-occurrence representative feature. The method may include, ifoccurrence-data correlating to the target-occurrence representativefeature is found, and if at least one of the input-selector andrecipient posses an access right, provide the correlatingoccurrence-data to the recipient.

Another embodiment provides an occurrence-data retrieval system. Thesystem includes a computing device operable to communicate with a datastorage device. The data storage device is operable to store a pluralityof instances of occurrence-data from remote data storages, each instanceof occurrence-data having a representative feature sensed respectivelyby a device associated with the remote data storage. The system alsoincludes an information security measure that protects instances ofoccurrence-data stored in the data storage device from unauthorizedaccess, and instructions, which when implemented in a computing device,cause the computing device to perform steps. The steps include receivefrom a redaction-selector a redaction selection corresponding to atarget-occurrence having a representative feature, and a tender of aredaction authorization. In response to the tendered redactionauthorization, determine if the redaction-selector possesses a redactionright, and automatically select a pattern recognition criteriacorresponding to the representative feature of the target-occurrence. Inresponse to the redaction selection corresponding to thetarget-occurrence, automatically search the plurality of instances ofoccurrence-data stored in the data storage device for data correlatingto the target-occurrence using the selected pattern recognitioncriteria. If the redaction-selector possesses a redaction right, redactan instance of the plurality of instances of occurrence-data from thedata storage device. The redacted instance of occurrence-data maycorrelate to the target-occurrence representative feature. The redactedinstance of occurrence-data may not correlate to the target-occurrencerepresentative feature.

A further embodiment provides a method. The method includes inputting aselection to a computing device corresponding to a target-occurrencehaving a representative feature, a recipient selection, and a tenderedaccess authorization. The method includes inputting a selection to thecomputing device corresponding to a plurality of instances of storedoccurrence-data protected by an information security measure. Eachinstance of occurrence-data originates from remote data storages,includes a representative feature sensed respectively by a deviceassociated with the remote data storage, and respectively correlates toan occurrence. If the tendered access authorization establishes anaccess right, receiving an output indicative of a search of theplurality of instances of stored occurrence-data for data correlating tothe target-occurrence. The data correlating to the target-occurrence maybe determined by a pattern recognition criteria automatically selectedin response to the target-occurrence. The plurality of instances ofoccurrence-data may be stored in a data storage device local to thecomputing device. The method may include inputting a redaction selectionand tendering a redaction authorization, and determining if a validredaction right is owned. If the tendered access authorizationestablishes a valid redaction right is owned, redacting an instance ofthe plurality of instances of occurrence-data from the storedoccurrence-data. The instance of occurrence-data may correlate to thetarget-occurrence representative feature. The redacted instance ofoccurrence-data may not correlate to the target-occurrencerepresentative feature.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the invention, together with features and advantages thereof,may be understood by making reference to the following description takenin conjunction with the accompanying drawings, in the several figures ofwhich like referenced numerals identify like elements, and wherein:

FIG. 1 illustrates a sensor node, or “mote”;

FIG. 2 illustrates a graph of a hypothetical data related to a sensedparameter that may define an occurrence;

FIG. 3 is a table illustrating several classes of occurrences, arelationship between an individual occurrence and at least onecharacteristic or attribute of the individual occurrences, andrepresentative features of the individual characteristics;

FIG. 4 illustrates a distributed sensor network;

FIGS. 5A and 5B include a flow diagram illustrating an exemplary processin which sensor data correlating to a target-occurrence is acquired froma sensor network and stored;

FIG. 6 illustrates a distributed sensor node occurrence-data archivaland retrieval system;

FIG. 7 is a flow diagram illustrating an exemplary process thataggregates and stores a plurality of instances of correlated sensor datain an occurrence-data archive;

FIG. 8 is a flow diagram that illustrates exemplary steps of a processthat searches and retrieves certain instances of stored correlatedsensor data from an occurrence-data archive;

FIG. 9 is a flow diagram illustrating exemplary steps of a process thatsearches a plurality of instances of occurrence data stored in a datavault or data lock box and provides an output;

FIG. 10 is a flow diagram illustrating exemplary steps of a processproviding the output of FIG. 9; and

FIG. 11 is a flow diagram illustrating exemplary steps of a process thatredacts a selected instance of occurrence data from the plurality ofinstances of stored occurrence data described in conjunction with FIG.9.

DETAILED DESCRIPTION

In the following detailed description of exemplary embodiments,reference is made to the accompanying drawings, which form a parthereof. The detailed description and the drawings illustrate specificexemplary embodiments by which the invention may be practiced. Otherembodiments may be utilized, and other changes may be made, withoutdeparting from the spirit or scope of the present invention;

Throughout the specification and claims, the following terms take themeanings explicitly associated herein unless the context dictatesotherwise. The meaning of “a”, “an”, and “the” include pluralreferences. The meaning of “in” includes “in” and “on”.

FIG. 1 illustrates a sensor node 20, or “mote,” many of which can becombined to form a sensor network. The sensor node 20 may be of varioussizes, and may be as small as a quarter coin, or smaller, as sensor nodesizes are now in the millimeter range. The sensor node 20 includes apower source 22, a logic circuit/microprocessor 24, a storage device 25,a transmitter (or transceiver) 26, a communications coupler 28 coupledto the transmitter 26, and a sensor element 30. Alternatively, the motemay be unpowered or passive, drawing its power from a reader or anothersource.

In the illustrated embodiment, the power source 22 provides power to thesensor node 20. For example, the power source 22 may include a battery,a solar-powered cell, and/or a continuous power supply furnished by anexternal power source, such as by connection to a power line. By way ofexample, the storage device 25 includes any computer readable media,such as volatile and/or nonvolatile media, removable and/ornon-removable media, for storing computer data in permanent orsemi-permanent form, and can be implemented with any data storagetechnology. Alternatively, the storage device 25 may store data in aform that can be sampled or otherwise converted into a form storable ina computer readable media.

The transmitter 26 transmits a data signal. In an optional embodiment,the transmitter 26 both receives and transmits data signals(transceiver). A “data signal” includes, for example and withoutlimitation, a current signal, voltage signal, magnetic signal, oroptical signal in a format capable of being stored, transferred,combined, compared, or otherwise manipulated. The transmitter 26 mayinclude wireless, wired, infrared, optical, and/or other communicationstechniques, for communication with a central computing device or centralstation, and optionally other sensor nodes, using the communicationscoupler 28. The communications coupler 28 may include an antenna forwireless communication, a connection for wired connection, and/or anoptical port for optical communication.

The sensor node 20 may include any type of data processing capacity,such a hardware logic circuit, for example an application specificintegrated circuit (ASIC) and a programmable logic, or such as acomputing device, for example, a microcomputer or microcontroller thatinclude a programmable microprocessor. The embodiment of the sensor node20 illustrated in FIG. 1 includes data-processing capacity provided bythe microprocessor 24. The microprocessor 24 may include memory,processing, interface resources, controllers, and counters. Themicroprocessor 24 also generally includes one or more programs stored inmemory to operate the sensor node 20. If an embodiment uses a hardwarelogic circuit, the logic circuit generally includes a logical structurethat operates the sensor node 20.

The sensor node 20 includes one or more sensor elements 30 that arecapable of detecting a parameter of an environment in which the sensornode is located and outputting a data signal. The sensor element 30 maydetect at least one parameter from a group of optical, acoustic,pressure, temperature, thermal, acceleration, magnetic, biological,chemical, and motion parameters. The optical parameter may include atleast one from a group consisting of infrared, visible, and ultravioletlight parameters. For example and without limitation, the sensor element30 may include a photo sensor to detect a level or change in level oflight, a temperature sensor to detect temperature, an audio sensor todetect sound, and/or a motion sensor to detect movement. The sensorelement 30 may include a digital image capture device, such as forexample and without limitation, a CCD or CMOS imager that captures datarelated to infrared, visible, and/or ultraviolet light images.

Typically, the sensor node 20 automatically acquires data related to aparameter of the sensor node environment, and transmits data to acentral computing device. For example, the sensor element 30 in a formof an acoustic sensor may acquire sound levels and frequencies, andtransmit the data related to the levels and frequencies along with atime track using the transmitter 26 and the communication coupler 28.The acquisition may be on any basis, such as continuously,intermittently, sporadically, occasionally, and upon request. In analternative embodiment, the time track may be provided elsewhere, suchas a device that receives the sensor data.

By way of further example and without limitation, the sensor element 30in a form of an optical digital camera may periodically acquire visualimages, such as for example, once each second, and to transmit the datarelated to visual images along with a time track. In another example,the sensor element 30 in the form of a temperature sensor may detecttemperature changes in two-degree temperature intervals, and to transmiteach two-degree temperature change along with the time it occurred. Eachof the above examples illustrates a sequence, ranging from continuousfor acoustical detection to a per occurrence basis for two-degreetemperature changes.

The sensor element 30 may sense operational parameters of the sensornode 20 itself, such as its battery/power level, or its radio signalstrength. Sensor data, including a data related to a sensed parameter,is transmitted from the sensor node 20 in any signal form via thetransmitter 26 and the communications coupler 28, to a receiver. Thereceiver may be, for example, another sensor node 20, a centralcomputing device, or any other data receiver. The sensor data mayinclude a time and/or date that the data related to a parameter wasacquired.

The sensor node 20 may include a unique identifier, and is operable tocommunicate the identifier in an association with its sensed parameter.In an alternative embodiment, the sensor node 20 may include aconfiguration that determines its location, for example, by a GPSsystem, by triangulation relative to a known point, or by communicationwith other sensor nodes. Alternatively, the location of the sensor node20 may be a known parameter established previously. Similarly, locationidentification may be associated with data originated and/or forwardedby the sensor node.

FIG. 2 illustrates a graph 50 of a hypothetical chronological sequence52 of a sensed parameter that may define an occurrence. The sequence 52illustrates a chronological sequence of a parameter that might beoutputted by a sensor node, and is plotted on the graph 50 with time ona x-axis and amplitude on a y-axis. The sinusoidal sequence 52 includesseveral representative features. A first representative feature is thatthe sequence 52 includes only two frequencies, A and B. A secondrepresentative feature is that each frequency lasts for three cyclesbefore the sequence 52 changes to the other frequency. A thirdrepresentative feature is that the sequence 52 amplitude is generallythe same over the time T.

For example, assume that an individual user is seeking datarepresentative of a car accident. The car accident is thetarget-occurrence. Further, assume that a characteristic of a caraccident is that an emergency vehicle may approach and/or be present atthe scene with its siren activated. Further, assume that it is knownthat a “do-dah, do-dah, do-dah” type siren used by some emergencyvehicles, such as fire, ambulance, or police, generates sound oracoustic waves that include the three features of the sequence 52. Also,assume that the sequence 52 represents a chronological sequence outputparameter by an acoustic sensor, such as element 30 of the sensor node20 of FIG. 1. Application of a pattern recognition criteria thatrecognizes the three above representative features of a sensor data thatincludes the sequence 52 is likely to locate sensor data representativeof the car accident occurrence that involved a presence of siren. Thesensor data may be either from a single sensor node 20 or a plurality ofsensor nodes 20.

By way of further example, if the occurrence of interest is passage ofan emergency vehicle siren through an intersection monitored by anacoustic sensor, a fourth representative feature would be a Dopplershift in the frequencies A and B on the passage of the vehicle.Expansion of the pattern recognition criteria to include recognition ofthe fourth feature is likely to locate sensor data representative of thepassage of the emergency vehicle. This example may be expanded whereeach intersection in a portion of a city is individually monitored bynetworked, distributed acoustic sensor nodes. Application of theexpanded pattern recognition criteria to the chronological sequences ofacoustic data outputted by the sensor nodes is expected to locate datarepresentative of the passage of the emergency vehicle through eachintersection, including a time of passage. Note that in this example,the siren is a selected target-occurrence while in the above example,the siren is a characteristic of the selected target-occurrence, the caraccident.

An occurrence includes anything that may be of interest, for example, toa user, a computing device, or machine. An occurrence may be or include,for example, a reference, an incident, an accident, an event, a realworld event, a change in a data sequence, and a change in a time domain.An occurrence may be a high-level matter such as a car crash or a riot,or a lesser-level matter, such as a siren or gun shot. This detaileddescription uses certain events having a sequence of at least oneparameter that may be detected by a sensor element to describeembodiments. However, the invention is not so limited.

FIG. 3 is a table illustrating several classes of occurrences, arelationship between an individual occurrence and at least onecharacteristic or attribute of the individual occurrences, andrepresentative features of the individual characteristics. Table of FIG.3 illustrates an anticipated relationship between occurrences,characteristics, and features.

For example, occurrence 1 of FIG. 3 is a car crash. A car crash includesa plurality of characteristics or attributes, such as (a) breakingglass, (b) impact noise, (c) tire screech, and (d) approach and presenceof emergency vehicles. Each of these characteristics has representativefeatures that can be sensed by one or more sensor nodes, such as thesensor node 20. Characteristic or attribute (a), breaking glass ofoccurrence 1, a car crash, is expected to include a representativefeature of sequential, high, and broadly-distributed sound frequenciesthat would be sensed by an acoustic sensor, such as the sensor element30 of FIG. 1.

Characteristic (d), approach and presence of emergency vehicles, isexpected to include a representative feature of a siren being sounded asan emergency vehicle approaches a car accident scene. A more detailedexample of representative features of a “do-dah, do-dah” siren patternis described in conjunction with FIG. 2 above. Other types of emergencysirens are expected to have different representative features.

By way of further example, a siren sound, which is a characteristic ofoccurrence 1, may also be considered an occurrence, and is shown asoccurrence 2 of FIG. 3. FIG. 3 also includes examples of fire, armoredconvey passage, and physical assault as high-level occurrences, and agun shot as a lesser-level occurrence.

As described above, each occurrence has certain known and/ordiscoverable features or representative features. In FIG. 2, the graph50 of the hypothetical chronological sequence 52 of a sensed parameterillustrates three representative features that may correspond to anoccurrence.

One or more representative features are selected for recognition ofsensor data representative of an occurrence of interest, which is alsoreferred to as a target-occurrence. Representative features are featuresthat correspond to a characteristic of an occurrence and provide a datarepresentation of the occurrence. A representative feature may beindividually selected by an input-selector, or automatically selected.Any suitable pattern recognition criteria, such as which may beexpressed in an algorithm, method and/or device, is used to identify oneor more of the selected representative features of a target-occurrencefor identification, location, retention, and/or retrieval of sensor datacorresponding to the target-occurrence. In certain embodiments, thepattern recognition criteria are computer implemented. “Patternrecognition criteria” as used in this specification may include anythingthat recognizes, identifies, or establishes a correspondence with, oneor more representative features of an occurrence. While the fields ofpattern recognition and artificial intelligence are sometimes consideredas separate fields, or that one is a subfield of the other, patternrecognition as used herein may include methods and/or devices sometimesdescribed as artificial intelligence. Further, pattern recognition mayinclude data or image processing and vision using fuzzy logic,artificial neural networks, genetic algorithms, rough sets, andwavelets. Further, a determination of which features are representativefeatures of a target-occurrence may also be determined using patternrecognition.

FIG. 4 illustrates a distributed sensor network 70 that includes anarray of sensor nodes 80, a central computing device 90, at least onedigital storage device, illustrated as a digital storage device 100, anda plurality of communications links. The sensor nodes of the pluralityof sensor nodes 80 are similar to the sensor node 20 of FIG. 1. Forpurposes of illustration, the sensor nodes are given reference numbersindicative of their communications tier with respect to the centralcomputing device 90. The first tier has reference numbers 82.1.1-82.1.N,and the second tier has reference numbers 82.2.1-82.2.N. Additionaltiers are not numbered for clarity. Each sensor node in the array ofsensor nodes 80 may sense a same parameter. Alternatively, a pluralitysensor nodes of the array of sensor nodes 80 may respectively sensedifferent parameters. For example, the sensor node 82.1.1 mayrespectively sense acoustical pressure and sensor node 82.1.2 mayrespectively sense temperature. The respective parameters sensed by theindividual sensor nodes may be mixed and matched in any manner toprovide a desired parameter description of the area in which the arrayof sensor nodes 80 are deployed.

In an embodiment, the individual sensor nodes of the plurality of sensornodes 80 of the sensor network 70 are typically distributed, that isthey are physically separated from each other. However, in certainembodiments, sensor nodes that sense different parameters are grouped inproximity to provide a more complete data related to a location.Further, in an embodiment, the sensor nodes of the array of sensor nodes80 are distributed over a geographical area. Such distributed sensorsmay include sensing “real world” environmental parameters occurring in alocale of each sensor, for example and without limitation, weather, carcrashes, and gunshots. In another embodiment, the sensor nodes of thearray of sensor nodes 80 are distributed in a manner to sense aparameter related to a physical entity, such as, for example and withoutlimitation, individual pieces of a distributed equipment, such astraffic lights or cell-phone transmission towers, or a locale, such asseats in a stadium.

An exemplary system implementing an embodiment includes a computingdevice, illustrated in FIG. 4 as a central computing device 90. In itsmost basic configuration, the computing device 90 typically includes atleast one central processing unit, storage, memory, and at least someform of computer-readable media. Computer readable media can be anyavailable media that can be accessed by the computing device 90. By wayof example, and not limitation, computer-readable media might comprisecomputer storage media and communication media.

Computer storage media includes volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof data such as computer readable instructions, data structures, programmodules or other data. Computer storage media includes, but is notlimited to, RAM, ROM, EPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to store thedesired data and that can be accessed by the computing system 90. Thecomputer storage media may be contained within a case or housing of thecomputing device 90, or may be external thereto.

Communication media typically embodies computer-readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information and/or delivery media. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal. By wayof example, and not limitation, communication media includes wired mediasuch as a wired network or direct-wired connection, and wireless mediasuch as acoustic, radio frequency, infrared, and other wireless media.Combinations of any of the above should also be included within thescope of computer-readable media. Computer-readable media may also bereferred to as computer program product.

The digital storage device 100 may be any form of a computer datadigital storage device that includes a computer storage media, includingthe forms of computer storage media described above. The digital storagedevice 100 may be a local digital storage device contained within a casehousing the computing device 90. Alternatively, the digital storagedevice 100 may be a local and external digital storage device proximateto the computing device 90, or remote to the computing device, and thatcoupled to the computing device 90 in either case by a communicationslink 99.

The computing device 90 also includes communications ports that allowthe computing device to communicate with other devices. Morespecifically, the computing device 90 includes a port 97 for a wiredcommunication link, such as the wired communication link 102 providingcommunications with at least one sensor node of the array of sensornodes 80. The computing device 90 also includes a wireless transceiveror receiver coupled with a communications coupler, such as the antenna96, for wireless communication over a link, such as the wirelesscommunication link 104. The wireless communications link 104 provideswireless communications with at least one sensor node of the array ofsensors devices 80. The wireless communication link 104 may include anacoustic, radio frequency, infrared and/or other wireless communicationlink. The computing device 90 further includes a port 98 for wired,wireless, and/or optical communication over a communication link 108with a network, such as a local area network, wide area network, andInternet. Such networking environments are commonplace in offices,enterprise-wide computer networks, intranets and the Internet. Thecommunications link may include an acoustic, radio frequency, infraredand other wireless connection.

The computing device 90 may also have input device(s) 94, such askeyboard, mouse, pen, voice input device, touch input device, etc. Thecomputing device 90 may further have output device(s) 92, such as adisplay, speakers, printer, etc. may also be included. Additionally, thecomputing device 90 may also have additional features and/orfunctionality.

The computing device 90 may be implemented in any suitable physicalform, including a mainframe computer, a desktop personal computer, alaptop personal computer, and a reduced-profile portable computingdevice, such as a PDA or other handheld device.

Logical operation of certain embodiments may be implemented as asequence of computer implemented steps, instructions, or program modulesrunning on a computing system and/or as interconnected machine logiccircuits or circuit modules within the computing system.

The implementation is a matter of choice dependent on the performancerequirements of the computing system implementing and embodiment. Inlight of this disclosure, it will be recognized by one skilled in theart that the functions and operation of various embodiments disclosedmay be implemented in software, in firmware, in special purpose digitallogic, or any combination thereof without deviating from the spirit orscope of the present invention.

FIGS. 5A and 5B include a flow diagram illustrating an exemplary process120 in which sensor data correlating to a target-event is acquired froma sensor network and stored. In certain embodiments, the process 120 isimplemented in a central computer, such as the computing device 90 ofFIG. 4. In other embodiments, at least a portion of the process 120 isimplemented in a sensor node of an array of sensor nodes, such as thesensor node array 80 of FIG. 4.

After a start block, the process 120 moves to block 122. At block, 122,a computing device, such as the central computing device 90,continuously receives sensed data of at least one parameter from asensor node over a communications link. The sensor node may be anysensor node, such as the sensor node 82.1.2, 82.1.N, or 82.2.2 of thearray of sensor nodes 80 of FIG. 4. The communications link may be anycommunications link known in the art, for example and withoutlimitation, an optical, a wireless, and/or a wired link. For example,FIG. 4 illustrates the sensor node 82.1.2 communicating over the wiredcommunications link 102, and the sensor node 82.1.N communicating overthe wireless communications link 104. FIG. 4 also illustrates the sensornode 82.2.2 communicating over a wireless link 106 with the sensor node82.1.3, which then relays and communicates the data from the sensor node82.2.2 with the computing device 90 over the wired communications link102.

Optimally, the sensed data is transmitted at intervals and aggregatedinto the data related to the sensed at least one parameter by areceiving device. In an alternative embodiment, the sensed data may betransmitted continuously by the sensor node. Furthermore, in anotherembodiment, the sensed data may include continuously sampled data at apredetermined sampling rate, such as a temperature reading capturedduring the first minute of every five-minute interval, or such as adigital image captured once each second.

At block 124, the received sensed data is continuously stored in astorage device, such as the storage device I 00, as first sensor dataset. In an alternative embodiment, the first data set includes amulti-element data structure from which elements of the data related tothe sensed at least one parameter can be removed only in the same orderin which they were inserted into the data structure. In anotheralternative embodiment, the first data set includes a multi-element datastructure from which elements can be removed based on factors other thanorder of insertion.

At block 126, an input selection is received from an input-selector of atarget-event having at least one representative feature. In a certainembodiment, the input-selector includes a user, who inputs the selectionof the target-event using the user input device 94 of FIG. 4. The usermay select the target-event from a list of possible target-eventsdisplayed on the user output device 92. The list for example, may besimilar to the list of occurrences of FIG. 3. In other embodiments, theinput-selector includes a machine, or a program running on a computingdevice, such as the computing device 90.

In an embodiment, the input selection of the target-event may include aselection an event that is directly of interest. For example, a soundpattern of interest, such as the siren sound that is event 2 of FIG. 3.In another embodiment, the input selection of the target-event may beformulated in terms of a parameter that correlates to the event that isdirectly of interest. For example, where the event of interest is afire, the input may be formulated in terms of a siren sound indicatingan approach or presence of emergency vehicles. The siren sound ischaracteristic (a) of a fire, which is event 3 of FIG. 3.

In a further embodiment, the input selection of the target-event isformulated in terms of weighing and/or comparing several instances of asensed data of at least one parameter from a plurality of sensor nodesto determine which of the several instances provide a goodrepresentation of the target-event. For example, the input selection mayrequest the best sensed data from six sensor, such as the best senseddata from six sensors that heard a gun shot during a time period.

At block 128, a pattern recognition criteria corresponding to at leastone representative feature of the target-event is selected. In anembodiment, the method includes at least one representative feature ofeach possible target-event. The process automatically selects one ormore pattern recognition criteria for recognition of sensor datarepresentative of or corresponding to the target-event. In certainembodiments, the pattern recognition criteria are included with theprocess 120, or available to the process from another source. Forexample, pattern recognition criteria may be associated locally with thecomputing device 90, or available to it over a communications link, suchas the communications link 108. In a further embodiment, patternrecognition criteria are provided to the computing device by theinput-selector in conjunction with the input of selection of thetarget-event.

At block 132, in response to the input selection corresponding to thetarget-event, the first sensor data set is automatically searched fordata correlating to the at least one target-event representative featureusing the selected pattern recognition criteria.

In a certain embodiment, the received input selection of thetarget-event further includes a selection of a representative feature ofthe target-event. The inputted selection of a target-eventrepresentative feature may be any feature that the input-selectorchooses for searching sensor data. For example, the selectedrepresentative feature may include a time period and acoustic frequencycomponents. The acoustic frequency components may include a selectedfrequency pattern, such as a recognized word, set of words, breakingglass, dog bark, door opening, alarm, threshold acoustic level, andvoiceprint. The selected representative feature may include a selectedelectromagnetic pattern, such as a visible light, infrared light,ultraviolet light, and radar. In this embodiment, at block 128, apattern recognition criteria is automatically selected by instructionsin response to the selected representative feature. Further, at block132, the first sensor data set is automatically searched using thepattern recognition criteria selected in response to the inputtedrepresentative feature.

At decision block 134, a determination is made if sensor datacorrelating to the at least one target-event representative feature wasfound. If the sensor data is not found, the process branches to block138. If the sensor data is found, the process branches to block 136. Atblock 136, the instructions cause the computing device 90 to store thecorrelated sensor data in a retained data storage. The retained datastorage may be at any location. For example and without limitation, theretained data storage may be local to the central computing device 90,such its removable or non-removable media; it may included in thedigital storage device 100; or it may be a remote digital storage deviceassociated with the computing device 90 over a communications link, suchas the communications link 108. In an embodiment, access to the retaineddata storage is restricted to authorized users. After storage of thecorrelated sensor data in a retained data storage, the process moves toblock 138.

In certain embodiments, in addition to storing the sensor datacorrelating to at least one target-event representative feature, theprocess includes storing a portion of the sensor data that was sensedbefore the found target-event representative feature. In otherembodiments, the instructions include storing a portion of the sensordata that was sensed after the found target-event representativefeature. In still other embodiments, the instructions include storing aportion of the sensor data that was sensed both before and after thefound target-event representative feature. These embodiments allow dataoccurring before and/or after the representative features to be saved.

In another embodiment, the process includes assigning a tentativeevent-identifier to the correlated sensor data. For example, if thetarget-event is a fire, and if a search of the first data set for datacorrelating to at least one fire event representative feature findscorrelating sensor data, the process includes association of a tentativeevent-identifier, such as “fire,” with the correlated sensor data. Thetrial-event identifier is associated with the stored correlated sensordata at block 136.

At block 138, the data related to the sensed at least one parameter iscontinuously deleted from the first data set according to a deletionsequence. In an embodiment, the deletion sequence includes asubstantially first-in, first-out order. In another embodiment, thedeletion sequence includes a factor other than order of insertion intothe data set.

At block 142, the process returns to block 132 to search another portionof the continuously received sensed data. The process continues whilethe continuous sensed data is received. The instructions then move tothe stop block.

An embodiment provides a computer implemented process for searching thedata related to the sensed at least one parameter from the first dataset and storing correlated sensor data for both the target-event asdescribed above and another target-event before deletion of the datafrom the first data set. In an alternative embodiment, another inputselection is received corresponding to another target-event having atleast one representative event feature. The input selection is receivedin a manner substantially similar to block 126. In a mannersubstantially similar to block 128, another pattern recognition criteriais automatically selected corresponding to at least one of therepresentative features of the selected another target-event.

In a manner substantially similar to block 132, in response to the inputselection corresponding to the another target-event, the first sensordata set is automatically searched for data correlating to the at leastone target-event feature of the another target-event using the selectedpattern recognition criteria. In a manner substantially similar todecision block 134, if sensor data correlating to the at least onetarget-event representative feature of the another target-event isfound, the correlated sensor data is stored in the same retained datastorage used to store representative features of the first target-event,or another retained data storage.

A further embodiment includes substantially simultaneously storingcorrelated sensor data for the target-event from two sensor nodes, eachnode generating separate data related to a same or a different sensedparameter. In such an embodiment, two parallel instances of sensedparameters are searched by the computing device 90 of FIG. 3 for datacorrelating to at least one representative feature of the target-event.In a manner substantially similar to block 122, data related to a sensedparameter from a second sensor node of the plurality of distributedsensor nodes is continuously stored into a second sensor data set.

In a manner substantially similar to block 132, in response to the inputselection corresponding to the target-event, the second sensor data setis automatically searched for data correlating to the at least onetarget-event representative feature using the selected patternrecognition criteria. In a manner substantially similar to decisionblock 134, if sensor data correlating to the at least one target-eventrepresentative feature of the target-event is found in the second dataset, the second correlated sensor data is stored. The storage locationmay be the same retained data storage used to store representativefeatures of the first target-event, or another retained data storage.

Yet another embodiment provides a process that substantiallysimultaneously stores correlated sensor data for a plurality oftarget-events from a respective plurality of sensor nodes, each nodegenerating a separate data related to a same or a different sensedparameter. The manner and method of scaling the computer process 120 forthe parallel and substantially simultaneous storing of correlated sensordata may be done in any manner known to those in the art.

Another embodiment includes using the computing power and storage of asensor node, such as the sensor node 20 of FIG. 1, to run at least aportion of the process 120. In conjunction with block 126 of FIG. 5A,the target even input selection may be preloaded into the sensor node,or may be communicated to the sensor node over a communications link.Similarly, in conjunction with block 128, the pattern recognitioncriteria may also be preloaded into the sensor node, or may becommunicated to the sensor node over a communications link. At block136, the retained data storage that stores the correlated sensor datamay be local to the sensor node, such as the digital storage 25 ofFIG. 1. The process 120 includes the sensor node transmitting at least aportion of the stored correlated sensor data over a communications linkto a central computing device, such as the central computing device 90of FIG. 4. The process 120 may further include deleting the storedsensor data after the data has been communicated to the centralcomputing device. In an alternative embodiment, the process 120 includesthe sensor node transmitting the stored correlated sensor data to thecentral computing device in response to a pull by the central computingdevice. In another alternative embodiment, the process 120 includes thesensor node pushing the stored correlated sensor data to the centralcomputing device.

Alternatively, at block 136, the retained data storage may be thedigital storage device 100 of the central computing device 90 of FIG. 4.The process 120 may include instructions that cause the sensor node totransmit at least a portion of the found correlated sensor data to thedigital storage device 100 for an initial storage.

An embodiment includes a communication media embodying the process 120,which, when implemented in a computer, causes the computer to perform amethod. For example, in an embodiment where the process 120 isimplemented in a computing device, such as the computing device 90 ofFIG. 4, instructions embodying the process are typically stored in acomputer readable media, such as without limitation the storage mediaand memory of the computing device, and loaded into memory for use.

A further embodiment includes a method implementing the steps of thecomputerized process 120, and a computer readable carrier containinginstructions which, when implemented in a computer, cause the computerto perform the method of the computer process 120.

An exemplary system employing certain embodiments described above may beillustrated by a network system of distributed acoustic sensor nodesplaced on a plurality of city traffic lights. While the illustrativesystem describes the networked system as owned by the city maintainingthe traffic lights, the exemplary system may have any ownership, such asa private, public, and governmental, and may be used for any purpose,such as private, public, governmental, and military.

The exemplary system includes an orientation toward gathering andstoring acoustic event data for later identification and retrieval. Theindividual nodes may use the power supplied to the traffic light astheir power source, or alternatively, use long-life batteries or solarpower. The individual nodes may communicate with a central computingdevice by sending sensor data over the power lines serving the trafficlight, separate wire communication links, or wireless communicationslinks. An event-data storage program embodying certain embodimentsdescribed above is operating on the central computing device. Dependingon the city's need to accumulate sensor data and total digital datastorage space requirements, a digital storage device within the centralcomputing device case may be used, or at least one local larger capacitydevice proximate to the central computing device may be used.

In operation of the exemplary system, each sensor node transmits datarelated to sensed acoustic data generated by their acoustic sensorelement to the central computing device. While the sensed acoustic datamay be transmitted continuously by each sensor node, optimally in thisembodiment to conserve bandwidth, the data is temporarily stored in thesensor node and transmitted to the central computing device in batches.A portion of sensed acoustic data for each sensor node in the network,including an identification of the originating sensor node, is receivedby the event-data storage program operating on the central computingdevice and stored in a data set queue in the associated digital storagedevice. Optimally, the sensed acoustic data for each sensor node isstored in a separate data set queue. This illustrative systemcontemplates that two things occur before the sensed acoustic data isreceived. First, the event-data storage program receive at least onetarget-event input selection. Second, a pattern recognition criteriacorresponding to at least one of the representative features of thetarget-event be selected. For this exemplary system, the selectedtarget-events are a gunshot, siren, tire screech, and loud voices. Theevent-data storage program automatically searches each sensor data setfor senor data having representative features correlating to a gunshot,siren, tire screech, or loud voices using the selected patternrecognition criteria. If sensor data correlating to a representativefeature of a gunshot, siren, tire screech, and loud voices is found, theprogram stores the correlated sensor data in a retained data storage.The retained data storage may have sufficient capacity to archivecorrelated event-data for a predetermined time period, such as a week, amonth, a year, or multiple years.

Optimally, the program also associates and stores a tentativeevent-identifier, such as gunshot, siren, tire screech, or loud voices,with the correlated sensor data. The associated tentativeevent-identifier will allow city officials to search the correlatedsensor data by identifying and event from gunshot, siren, tire screech,or loud voices, and searching the retained data storage by tentativeidentifiers instead of what may be a more complicated search use patternrecognition criteria. After the batch sensed acoustic data is searched,the program automatically deletes the sensor acoustic data from the dataset queue. The deletion minimizes the amount of digital data storagenecessary in the system by saving only sensor data correlating toselected target-events.

While the above exemplary system includes gathering and storingevent-data on a non-real-time basis for later retrieval, an embodimentallows the system to perform real-time tentative identification of oneor more target-events and save correlating sensor data. For example,sensor nodes having sufficient computing capacity may be preloaded withone or more input target-event selections. Each sensor node wouldautomatically and in substantially real-time search sensor datagenerated by its local sensor element for sensor data correlating to theinput target-event selection. Instead of storing for later transmission,the found correlating sensor data would be immediately transmitted tothe central computing device and be available for use. The datatransmission may include associated tentative event-identifiers. Ineffect, the sensor nodes filter their acoustical data and only providesensor data to the central computing device that corresponds theinputted target-event selection. The event-data program may then storethe found correlating sensor data, and notify a user in substantiallyreal-time of receipt of data having the tentative target identifiers.The notification may be by a display on a monitor screen coupled withthe central computing device. The user may then listen to the correlatedsensor data and take appropriate action, such as notifying police orfire.

Another embodiment includes a mobile central computing device that auser takes into communication range with a network of remote sensornodes. A mobile computing device, such as a laptop and a reduced-profilecomputing device, provide mobility to the computing device 90. Themobility allows a user to take the central computing device 90 into thefield and within transmission range of certain sensor nodes of adistributed network of remote sensor nodes. The sensor nodes typicallyhave acquired and stored a plurality of sensor data sets, each sensordata set representing a respective feature sensed by a sensor element ofits respective sensor node. A communication link, typically a wirelesslink, is established between the computing device 90 and one or more ofthe sensor nodes of the array of sensor nodes 80 of the network ofremote or distributed sensor nodes 70. The user inputs a selection ofsensor data sets to be transmitted from the certain sensor nodes to thecomputing device 90. In response, a process running on the computingdevice 90 communicates with the one or more sensor nodes, extracts thesensor data sets, stores them, and provides a confirmation to the userthat the selected sensor data sets have been received. The usertypically will receive the confirmation and move the computing deviceinto communication proximity to other sensor nodes of the array ofsensor nodes 80. Typically, the stored plurality of sensor data sets aredeleted from the sensor nodes after transmission to the computing device90 to free-up storage.

FIG. 6 illustrates a distributed sensor node event-data archival andretrieval system 150. The system 150 includes a plurality of distributedsensor networks, illustrated as first, second, and third distributedsensor networks 70, 152, and 162 respectively. The distributed sensornetwork 70 is described in conjunction with FIG. 4, and the sensornetworks 152 and 162 are substantially similar to the sensor network 70.Each distributed sensor network includes an array of sensor nodes,illustrated as a first, second, and third arrays 80, 154, and 164respectively. Each sensor network also includes at least one centralcomputing device, illustrated as first, second, and third centralcomputing devices 90, 156, and 166 respectively, and includes aplurality of communications links. The arrays of sensor nodes 154 and164 are substantially similar to the array of sensor nodes 80 describedin conjunction with FIG. 4. For clarity, only several sensor nodes andtheir communications links are illustrated in the arrays 80, 154, and156 in FIG. 6.

The second and third central computing devices 156 and 166 aresubstantially similar to the first central computing device 90 of FIG.4. The second and third digital data storage devices 158 and 168, andthe associated communications links 159 and 169 that communicate withthose central computing devices are substantially similar to the firstdigital data storage device 100 and the first communications link 99,also as described in conjunction with FIG. 4.

The system 150 also includes an aggregating computing device 170 that issubstantially similar to the central computing device 90 of FIG. 4. Thewords “central,” “aggregating,” “collecting,” and “archival” are used inthis specification, including the claims, to identify certain devicesand to illustrate a possible network hierarchy environment of one ormore embodiments. These words do not limit the nature or functionalityof a device. The system 150 illustrates a possible network hierarchywhere, in an embodiment, a plurality of central computing devices,illustrated as the central computing devices 90, 156, and 166, receiveand store sensor node data from a plurality of sensor node arrays,illustrated as the sensor nodes of the arrays 80, 154, and 164respectively. The system 150 also illustrates a possible networkhierarchy where, in an embodiment, the aggregating computing device 170receives and stores, i.e., aggregates, sensor data acquired by aplurality of central computing devices, illustrated in FIG. 6 as centralcomputing devices 90, 156, and 166. In another embodiment, the computingdevice 170 may function as a central computing device providing sensordata it received and stored to another aggregating computing device (notillustrated).

The computing device 170 communicates with at least one remote digitaldata storage device, such as storage devices 100, 158, and 168, throughtheir associated computing devices 90, 156, and 166, respectively, usingone or more communications links. As illustrated in FIG. 6, theaggregating computing device 170 also includes communications ports thatallow the computing device to communicate with other devices. Thesecommunications ports are substantially similar to the communicationsports of the computing device 90 of FIG. 4. More specifically, thecomputing device 170 includes a sensor communication port 177 for awired communication link, such as the wire communication link 189,providing communications with the central computing device 156 and itsassociated digital data storage device 158. The computing device 170also includes a wireless transceiver or receiver coupled with acommunications coupler, such as an antenna 176, for wirelesscommunication over a communications link, such as a wirelesscommunication link 186. FIG. 6 illustrates the wireless communicationlink 186 coupling the computing device 170 and the computing device 166,and its associated digital data storage device 168. The computing device170 further includes a network communications port 178 for wired,wireless, and/or optical communication over a communication link, suchas the network communications link 188, for communication with anetwork, such as a local area network, wide area network, and Internet.FIG. 6 also illustrates a communications link 188 as network linkbetween the central computing device 90 and its associated digitalstorage device 99. The communications link 188 may include an acoustic,radio frequency, infrared and other wireless connection.

The system 150 also includes at least one digital storage device as anevent-data archive, illustrated as an archival digital data storagedevice 190, which may be substantially similar to the digital datastorage device 100 of FIG. 4. The archival digital storage device 190may be a local digital data storage device contained within a casehousing the computing device 170. Alternatively, the archival digitalstorage device 190 may be a local and external digital data storagedevice proximate to the computing device 170, or it may be remote to thecomputing device. The archival digital data storage device 190 iscoupled to the computing device in any event by a communications link179.

The aggregating computing device 170 may also have input device(s) 174,such as keyboard, mouse, pen, voice input device, touch input device,etc. The computing device 170 may further have output device(s) 172,such as a display, speakers, printer, etc. may also be included.Additionally, the computing device 170 may also have additional featuresand/or functionality.

FIG. 7 is a flow diagram illustrating an exemplary process 200 thataggregates and stores a plurality of instances of correlated sensor datain an event-data archive. After a start block, the process 200 moves toblock 202. At block, 202, a plurality of central computing devices, suchas the central computing devices 90, 156, and 166, each transmit aplurality of instances of correlated sensor data to an aggregatingcomputing device. The instances of correlated sensor data are typicallyacquired by a sensor node operable to sense at least one parameter, andeach instance has been correlated to an event having at least onerepresentative feature. The instances may be stored in one or moredigital data storage devices, such as the storage devices 100, 158, and168, associated with the central computing devices 90, 156, and 166,respectively. In an alternative embodiment, at least one digital datastorage device is remote to its associated computing device. The remotedigital data storage device may be included in one or more sensor nodes.

In the embodiment illustrated in FIG. 6, the correlated sensor data isaccessed from the storage devices 100, 158, and 168 by their associatedcentral computing devices 90, 156, and 166, and transmitted over theirassociated communications links 108, 186, and 189, to the aggregatingcomputing device 170. In an embodiment, each instance of the sensor datawas acquired by at least one sensor node of a plurality of distributedsensor nodes, and each sensor node is part of a network of sensor nodes.Further, each instance of correlated sensor data may include anassociated tentative event-identifier, which typically is generated andassociated when the instance of correlating sensor data was found.

In an alternative embodiment (not illustrated), instances of correlatedsensor data re pulled from the digital data storage devices in responseto a request communicated to their respective central computing devicesby the aggregating computing device 170. In another embodiment,instances of correlated sensor data are transmitted or pushed from thedigital data storage devices by their associated central computingdevice to the aggregating computing device 170.

At block 204, the plurality of instances of correlated sensor data arereceived. At block 206, the plurality of instances of correlated sensordata are stored in an aggregating digital data storage device, such asthe digital data storage device 190. The aggregating digital datastorage device may be referred to in this specification as an event-dataarchive. In an alternative embodiment, the plurality of instances ofsensor data stored in the event-data archive are protected by aninformation security measure. Such a protected or secured stored dataarrangement may be referred to in this specification as a “data vault”or “data lock-box.”

The information security measure typically includes providing at leastone of maintaining information confidentiality, maintaining informationintegrity, and limiting access to authorized persons. The informationsecurity measure may be any security measure known to those skilled inthe art, and at a selected level commensurate with the value of theinformation contained in the instances of correlated sensor data and anyloss that might accrue from improper use, disclosure, or degradation.The information security measure may be implemented in software,hardware, infrastructure, networks, or any other appropriate manner. Inan embodiment, the information security measure may be associated withthe digital data storage device, the plurality of instances ofcorrelated sensor data, and/or a computing device having a communicationlink with the digital data storage device.

Next, at block 208 the process 200 waits for more event data. Ifadditional event data is received, the process moves to block 204 andreceives the additional event data. The process 200 then proceeds to thestop block. In an alternative embodiment, the process 200 includesdeleting at least a portion of the instances of correlated sensor datafrom the digital data storage devices 100, 158, and 168 after theinstances have been transmitted to the aggregating computing device.

The process 200, when implemented in a computing device, causes thecomputing device to perform certain steps. For example, in an embodimentwhere the process 200 is implemented in a computing device, such as theaggregating computing device 170 of FIG. 6, the instructions aretypically stored in a computer readable media, such as the storage mediaand/or memory of the computing device, and loaded into memory for use.In certain embodiments, the process 200 aggregates instances of sensordata correlating to an event from a plurality of remote digital datastorage devices, and stores those instances on a digital data storagedevice associated with an aggregating computer as an event-data archive,such as the archival digital data storage device 190 of FIG. 6.

FIG. 8 is a flow diagram that illustrates exemplary steps of a process220 that searches and retrieves certain instances of stored correlatedsensor data from an event-data archive. After a start block, the process220 moves to block 222. At block 222, an input selection is receivedfrom an input-selector corresponding to a target-event having at leastone representative feature. The input-selector may include any entity,such as a machine, a computing device, and a user.

The input selection optimally further includes the input-selectortendering an access authorization, which is used to determine if theinput-selector is a trusted entity. The tendered access authorizationmay be by any method or device required by a security measure protectingthe instances of stored sensor data from unauthorized access, such asfor example, a password, and thumb print. For example, a trusted entitymay be a user, machine, or computing device, identified on a list oftrusted parties. For example, the list of trusted parties may includeemployees and/or computing devices associated with the owner of thesensor network system. The tendered access authorization may be theinput-selector's personal identification. Further, a trusted entity maybe a member of a certain class, such as uniformed law enforcementofficers, or computing devices maintained by agencies that employuniformed law enforcement officers. For example, uniformed lawenforcement officers may include members of the Federal Bureau ofInvestigation, Alcohol Tobacco and Firearms, state patrol, countysheriffs, and local police. Another example of a trusted party class isa prosecuting attorney, a defense attorney, and a judicial officer.

In a less preferred embodiment, the instances of stored sensor data arenot protected by a security measure, and the input selection does notinclude tender of an access authorization.

At block 224, a decision operation determines if the tendered accessauthorization establishes the input-selector is a trusted entity andpossesses an access right to the stored correlated sensor data. If theinput-selector is a trusted entity and has an access right, the processbranches to block 226. If the input-selector does not posses an accessright, the process branches to the end block. If a security measure isnot protecting the instances of stored sensor data, then the decisionblock 224 is not necessary and the process moves from decision block 222to block 226.

At block 226, a pattern recognition criteria is selected correspondingto at least one representative feature of the target-event. The criteriais selected in a manner substantially similar to block 128 described inconjunction with FIGS. 5A and 5B, including the alternative embodiments.At block 228, in response to the input selection corresponding to thetarget-event, a plurality of instances of stored sensor data areautomatically searched for data correlating to the target-event usingthe selected pattern recognition criteria.

At decision block 232, a decision operation determines if sensor datacorrelating to the at least one target-event representative feature isfound. If the sensor data correlating to the target-event is not found,the process branches to block 236, where a message equivalent to “nodata found” is provided. If sensor data correlating to the target-eventis found, the process branches to block 234.

At block 236, the found correlated sensor data is provided. In anembodiment, the input-selector is the recipient of the correlated sensordata. In another alternative embodiment, a third party is the recipientof the correlated sensor data. The third party may include a machine, acomputing device, and a user. In a further embodiment, theinput-selector selects a third party recipient of the correlated sensordata. In an alternative embodiment, the process at block 222 furtherincludes receiving an access authorization of the third part tendered bythe input-selector, and the process at decision block 224 furtherincludes determining if the third party recipient possesses an accessright before providing the correlated sensor data to the third party.The process 220 then moves to the end block.

In a further alternative embodiment of the process 220, the search atblock 228 proceeds in response to an input-selector designation of atarget tentative-event-identifier. In this embodiment, the receivedplurality of instances of correlated sensor data each include anassociated tentative-event-identifier. At block 222, the receivedtarget-event selection includes an input selection corresponding to atarget tentative event-identifier. If a target tentativeevent-identifier is selected and no reason exists to search for arepresentative feature, the block 226 may be bypassed. At block 228, inresponse to the input selection corresponding to the target tentativeevent-identifier, the plurality of instances of sensor data areautomatically searched-for data correlating to the target tentativeevent-identifier. If any event data is found correlating to the targettentative event-identifier at decision block 232, the found sensor datacorrelating to the target tentative event-identifier is provided atblock 234.

The process 220, when implemented in a computing device, causes thecomputing device to perform steps. In certain embodiments, the process220 implements a process that searches and retrieves instances of storedsensor data from an event-data archive protected by a security measure,such as the archival digit data storage device 190 coupled to thecomputing device 170 of FIG. 6. In other embodiments, the process 220uses a local computing device to search and retrieve instances of storedsensor data from remote digital data storage devices, such as thedigital data storage device 168.

The process 220, when implemented in a computing device, causes thecomputing device to perform certain steps. For example, in an embodimentwhere the process 220 is implemented in a computing device, such as theaggregating computing device 170 of FIG. 6, the instructions aretypically stored in a computer readable media, such as the storage mediaand/or memory of the computing device, and loaded into memory for use.

An exemplary system employing certain embodiments described above may beillustrated by three network systems of distributed sensors, and anaggregating computing device. Referring to FIG. 6, the illustrativeexemplary system includes the previously described exemplary networksystem of distributed acoustic sensors placed on city traffic lights asthe first sensor network 70, an exemplary network system of distributeddigital image capture devices located in city parking garages and lotsas the second sensor network 152, and an exemplary network ofdistributed heat/fire thermal sensors located in city buildings as thethird sensor network 162. Each exemplary sensor network automaticallystores correlated sensor data in an associated retained data storage,such as the digital data storage devices 100, 158, and 168.

As with FIG. 4, while the illustrative exemplary system describes thenetworked system as owned by the city, the illustrative exemplary systemmay have any ownership, such as a private, public, and governmental, andmay be used for any purpose, such as private, public, governmental, andmilitary. Further, the sensor networks may have different owners. Forexample, the first sensor network 70 may be owned by the city, thesecond sensor network 152 may be privately owned by a parking garageoperator, and the third sensor network 162 may be privately owned by afire alarm company.

The illustrative exemplary system further includes an aggregatingcomputing device communications linked to the sensor networks, such asthe aggregating computing device 170 and its archival digital datastorage device 190. The central computing devices of the three networkstransmit the correlated sensor data from their retained data storage tothe aggregating computing device. The aggregating computing devicereceives and stores the correlated sensor data from the three networksin an event-data archive on its associated digital data storage device,such as device 190. The event-data archive includes a data structuresuitable for later search and retrieval. The event-data archive issubject to an information security measure that protects the sensor datastored in the event-data archive from unauthorized access. The securitymeasure is controlled by the aggregating computing device. The centralcomputing devices delete the correlated sensor data from theirassociated retained data storage after transmission to the aggregatingcomputing device. This frees storage space for the constant stream ofadditional correlated sensor data that is continuously transmitted bysensor nodes of their respective sensor networks.

A requesting entity may be an employee or official of an owner oroperator of one of the sensor networks, or may be a potentiallyauthorized person, machine, network or other entity. A requesting entitydesiring sensor data on an event, such as shooting, enters a gunshottarget-event selection on a user input device of the aggregatingcomputing device, and tenders an identification number as an accessauthorization. In this example, the gunshot (event 6 of FIG. 3) may haveoccurred near an intersection controlled by a city traffic light at aknown date.

An event-data retrieval process operating on the aggregating computingdevice receives the target-event selection and the employeeidentification number. The process determines that the requesting entityis a trusted entity and possesses an access right. In response to thegunshot target-event selection, the event-data retrieval processautomatically selects a pattern recognition criteria corresponding to atleast one representative feature of a gunshot. Then, the event-dataretrieval process in response to the gunshot input selection,automatically searches the event-data archive for instances of acousticsensor data correlating to the at least one representative feature of agunshot on the known date. Correlating found instances of archivedsensor data are provided to the requesting entity, or a trusted thirdparty selected by the requesting entity.

In further reference to FIG. 8, another embodiment provides a processthat searches and retrieves certain instances of stored correlatedsensor data from an event-data archive. After a start block, theembodiment includes receiving an input selection from an input-selector,similar to the process 220 at block 222. The input selection correspondsto a target-occurrence having a representative feature. A filtercorresponding to the representative feature of the target-occurrence isselected. A plurality of instances of occurrence data stored in a dataset are filtered for data correlating to the target-occurrencerepresentative feature a using the selected filter. Each instance of thestored occurrence data has a representative feature. An outputresponsive to the filtering is provided. The process then ends. Thefiltering step may further include automatically filtering the datastored in the data set. In a further embodiment, the output responsiveto the filtering correlates to a target-occurrence representativefeature, which is stored in another data set. Alternatively, in anotherembodiment, the output responsive to the filtering does not correlate toa target-occurrence representative feature. The non-correlating outputis stored in anther data set.

FIG. 9 is a flow diagram illustrating exemplary steps of a process 300that searches a plurality of instances of event data stored in a datavault or data lock box and provides an output. Each instance of theevent data has at least one representative feature, is stored in adigital data storage device, and is protected by an information securitymeasure. The digital data storage device may be a local digital datastorage device or a remote digital data storage device. The informationsecurity measure may be associated with the digital data storage device,the plurality of instances of stored event data, and/or a computingdevice having a communication link with the digital data storage device.In another embodiment, the digital data storage device includes aportable digital data storage device, such as an external hard drive, aDVD, a CD, a floppy disk, and a flash memory device. In a furtherembodiment, the event data includes sensor data generated by a pluralityof networked sensor nodes.

The process 300 is similar to the process 220. After a start block, theprocess 300 moves to block 302. At block 302, an input selection isreceived from an input selector, the input selection corresponding to atarget-event having at least one representative feature. The receivedinput selection further includes an output recipient selection and atendered access authorization.

At block 304, in response to the tendered access authorization, adecision operation determines if an access right to the plurality ofinstances of stored event data protected by the information securitymeasure is possessed by at least one of the input-selector and therecipient. If the decision operation determines that either theinput-selector and/or the recipient are a trusted entity and posses anaccess right to the instances of stored event data, the process branchesto block 306. If neither the input-selector nor the recipient is atrusted entity, the process branches to the end block. In an alternativeembodiment, the input-selector and the recipient must each possess anaccess right.

At block 306, a pattern recognition criteria is selected correspondingto at least one representative feature of the target event. The criteriais selected in a manner substantially similar to block 128 described inconjunction with FIGS. 5A and 5B, and to block 226 described inconjunction with FIG. 8, including the alternative embodiments.

At block 308, in response to the input selection corresponding to thetarget event, the plurality of instances of stored event data areautomatically searched for data correlating to the at least onetarget-event representative feature using the selected patternrecognition criteria.

At decision block 312, a decision operation determines if event datacorrelating to the at least one target-event representative feature wasfound. If the event data correlating to the target-event representativefeature was not found, the process branches to block 316, where amessage equivalent to “no data found” is provided. If event datacorrelating to the target was found, the process branches to block 314.At block 314, an output indicative of the result of the automatic searchat block 308 is provided to the recipient.

In a further alternative embodiment of the process 300, the search atblock 308 proceeds in response to an input-selector designation of atarget tentative event-identifier in a substantially similar manner asthe process 200 described in conjunction with FIG. 8.

The process 300, when implemented in a computing device, causes thecomputing device to perform certain steps. For example, in an embodimentwhere the process 300 is implemented in a computing device, such as theaggregating computing device 170 of FIG. 6, the instructions aretypically stored in a computer readable media, such as the storage mediaand/or memory of the computing device, and loaded into memory for use.

FIG. 10 is a flow diagram illustrating exemplary steps of a process 350providing the output of the block 314 of FIG. 9. The illustratedembodiment includes a set of possible outputs 360 from the output atblock 314. The set of possible outputs 360 illustrated in FIG. 10includes a first subset of outputs for event data correlating to thetarget-event representative feature, and a second subset of outputs forevent data not correlating to the target-event representative feature,i.e., non-correlating. The first subset includes a correlating tentativeevent-identifier 362, a degraded correlating event-data representation363, and a correlating event data 364. The second subset includes anon-correlating tentative event-identifier 366, a degradednon-correlating event-data representation 367, and a non-correlatingevent data 368. The process 350 at block 314 includes a defaultconfiguration, indicated by solid hierarchal lines 361, that providesthe correlating tentative event-identifier 362 and the non-correlatingtentative event-identifier 366. In an alternative embodiment, the outputconfiguration provides the degraded correlating event-datarepresentation 363 and the degraded non-correlating event-datarepresentation 367. In another alternative embodiment, the outputconfiguration provides only the correlating event data 364.

At block 314, the initial output is provided to the input-selectorand/or recipient in any manner and using any output device, such asbeing displayed on a monitor of a computing device. For example, theoutput may include displaying a table having columns that include anevent data date, a tentative event identifier, and acorrelating/non-correlating status. Individual instances of theplurality of instances of stored event data are individually displayedin rows of the table. For example, in response to a target-eventselection of a gunshot, which is event 6 of FIG. 3, one row may displaya date of May 17, 2004, a tentative event-identifier of a “gunshot,” anda status of “correlating.” Another row may display the same date of May17, 2004, a tentative event-identifier of “unknown” because nocorrelation to a representative feature of a gunshot was found, and astatus of “non-correlating.” In an alternative embodiment, the output atblock 314 may include a ranking for at least two instances of thecorrelating event data in a hierarchy of the found correlating eventdata. For example, if the provided output in the above example includesa plurality of events having “gunshot” tentative event-identifiers, theprovided output may further include a relative or absolute ranking basedon the acoustic intensity of the respective events as an aid to therecipient in evaluating the event data.

At block 322, an event-data selection is received from theinput-selector, who may be the recipient. The selection corresponds toat least one of the instances of event data provided by the process atblock 314 and requests provision of more detail related to the providedinstances. In the default configuration, the input selection maycorrespond to a tentative event-identifier. For example, the inputselection may request provision of degraded correlating event datacorresponding to the event of May 17, 2004, and tentatively identifiedas gunshot.

At block 324, the selected event data is provided in a form of degradedcorrelating data. In an embodiment, the degraded correlating event dataincludes sufficient data for the recipient to make a preliminarydetermination whether the event appears to be a gunshot. For example therecipient may listen to the degraded data or view a display of atime-frequency analysis of the degraded data. The process 350 thenterminates at the end block.

If the recipient possesses an access authorization for the correlatingevent data 364, the event-data selection may include receiving anotherinput selection that requests that the correlating event-data beprovided. The process at block 316 receives the another event-dataselection, and at block 318 provides the output. Continuing with theabove example, the recipient may request complete event data (364) fromall the sensors that correlates to the gunshot.

The process 350, when implemented in a computing device, causes thecomputing device to perform certain steps. For example, in an embodimentwhere the process 350 is implemented in a computing device, such as theaggregating computing device 170 of FIG. 6, the instructions aretypically stored in a computer readable media, such as the storage mediaand/or memory of the computing device, and loaded into memory for use.

FIG. 11 is a flow diagram illustrating exemplary steps of a process 400that redacts a selected instance of event data from the plurality ofinstances of stored event data described in conjunction with FIG. 9.After a start block, the process moves to block 402, where a redactionselection and a tendered redaction authorization are received. Theredaction selection includes a selection of at least one of theplurality of instances of event data. In an embodiment, the redactionselection may be correlated with the provided output at block 314 ofFIGS. 9 and 10. Using the above example where a plurality rows aredisplayed in a table on a monitor, individual target-event-identifiersmay be hyperlinked. This allows an input-selector to select an event forredaction by activating a link in a displayed row.

At block 404, in response to the tendered redaction authorization, adecision operation determines if at least one of the input-selector andthe recipient possess a redaction right to the plurality of instances ofstored event data protected by the information security measure. If thedecision operation determines that either the input-selector and/or therecipient are a trusted entity and posses a redaction right, the processbranches to block 406. If neither the input-selector nor the recipientis a trusted entity, the process branches to the end block.

At block 406, the selected event data is redacted from the plurality ofinstances of the stored event data. The redacted instance of event datamay or may not correlate to the at least one target-event representativefeature. The process 400 then terminates at the end block.

The process 400, when implemented in a computing device, causes thecomputing device to perform certain steps. For example, in an embodimentwhere the process 400 is implemented in a computing device, such as theaggregating computing device 170 of FIG. 6, the instructions aretypically stored in a computer readable media, such as the storage mediaand/or memory of the computing device, and loaded into memory for use.

An exemplary system employing certain embodiments described inconjunction with FIGS. 9-11 may be illustrated using the exemplarysystem of the three network systems of distributed sensors and theaggregating computing device previously described in conjunction withFIG. 8. Continuing with the previous illustration, the event-dataarchive associated with the aggregating computing device now containscorrelating event data acquired from the three-network system over time,such as a year. The gunshot has resulted in litigation, and thelitigants request discovery of correlating event data in the city's datavault, which is the city's event-data archive protected by a securitymeasure. The city is willing to provide relevant instances event data tothe litigants and a court, but unwilling to provide other instances ofevent data based on proprietary and citizen privacy concerns.

A trusted person designated by the court and given an accessauthorization by the city provides an input selection corresponding tothe gunshot event of May 17, 2004. For example, the trusted person maybe a neutral expert, an expert witness for a party, and a magistrate.The input selection is received by an archival event-data processdescribed in conjunction with FIGS. 9-11, and a determination made thatthe trusted person acting as an input-selector possesses an access rightto the data vault. In response to the gunshot target-event selection,the archival event-data retrieval process automatically selects apattern recognition criteria corresponding to at least onerepresentative feature of a gunshot. The archival event-data retrievalprocess, in response to the gunshot input selection, automaticallysearches the event-data archive for instances of acoustic sensor datacorrelating to the at least one representative feature of a gunshot onthe known date.

An initial output indicative of the search result is provided to thetrusted person. In the exemplary embodiment, the default outputconfiguration described above provides a table displaying thecorrelating tentative gunshot-identifiers (362) and the non-correlatingtentative gunshot-identifiers (366) in rows. The trusted person providesan event-data selection that corresponds to at least one of theinstances of tentative gunshot-identifiers initially provided by theprocess. For example, an initial output may indicate that a plurality ofsensors generated acoustical data correlating to at least onerepresentative feature of a gunshot, and the input selector selectsthree of these instances. The event-data selection is received from theinput-selector, and the archival event-data retrieval process providesthe trusted person with the three selected instances of degradedcorrelating event data corresponding to the gunshot. The trusted personlistens to the three instances of degraded event data. If the trustedperson concludes two of the three instances of event data relate to thegunshot, the trusted person then requests and is provided with the twocomplete event data for the two instances.

Another embodiment of the exemplary archival event-data process providesa redaction whereby the city through a representative, or the trustedperson, may remove certain instances of event data from the plurality ofinstances of event data in the city's data vault. The redacted datavault may then be given to a third party much like a redacted paperdocument. Preferably, the city retains a duplicate of their data vaultprior to beginning the redaction process. The process includes receivingthe redaction selection from the trusted party, and a tender of aredaction authorization. For example, the redaction selection may beformulated in terms of redacting all event data except for the threeselected instances of event data correlating to a gunshot.Alternatively, the redaction selection may be inverted to redact onlythe three selected instances of event data correlating to a gunshot.Since redaction involves alteration of data from the data vault, thecity may require a separate redaction right in addition to the accessright.

The process determines that the trusted party possesses a redactionright. In response to the redaction selection, all but the threeinstances of event data are redacted from the data vault. The data vaultand the three selected instances of gunshot data stored therein may bemade accessible to others involved in the litigation.

Although the present invention has been described in considerable detailwith reference to certain preferred embodiments, other embodiments arepossible. Therefore, the spirit or scope of the appended claims shouldnot be limited to the description of the embodiments contained herein

What is claimed is:
 1. A computer-implemented method for processingsensor data, the method comprising: electronically and automaticallyreceiving, by at least one specially programmed central processing unit,via at least one communication link, sensed data from at least onesensor network; wherein the at least one sensor network comprises aplurality of remotely located recording sensor nodes; wherein the senseddata comprises environmental data for each environment in which eachremotely located recording sensor node is located; wherein theenvironmental data is being continuously captured, over a plurality oftime periods, by the plurality of remotely located recording sensornodes, based, at least in part, on a plurality of environmentalparameters; wherein the plurality of environmental parameterscomprises: 1) at least one temperature environmental parameter, and 2)at least one motion environmental parameter; wherein each of theplurality of remotely located recording sensor nodes captures theenvironmental data for at least one respective environmental parameter;automatically analyzing, by the at least one specially programmedcentral processing unit, the sensory data, captured over at least onefirst time period of the plurality of time periods, to identify aplurality of representative features within the sensed data, whereineach representative feature of the plurality of representative featuresis represented by particular environmental data of the at least onerespective environmental parameter; electronically storing, in at leastone non-transient database, by the at least one specially programmedcentral processing unit, data representative of the plurality ofrepresentative features related to the at least one first time period ofthe plurality of time periods; electronically establishing, based on atleast one pattern recognition criterion, by the at least one speciallyprogrammed central processing unit, a first correspondence of at leastone first representative feature from the plurality of representativefeatures to at least one first characteristic of at least one firstoccurrence and a second correspondence of at least one secondrepresentative feature from the plurality of representative features toat least one second characteristic of the at least one first occurrence,wherein at least one first instance of the at least one first occurrencehas occurred within the at least one first time period of the pluralityof time periods; electronically discovering, based on the firstcorrespondence and the second correspondence, by the at least onespecially programmed central processing unit, at least one secondinstance of the at least one first occurrence within the sensed datacaptured over at least one second time period of the plurality of timeperiods, wherein the at least one first occurrence has taken place in atleast one first environment associated with at least one first remotelylocated recording sensor node; and electronically causing, based on thediscovery of the at least one second instance of the at least one firstoccurrence in the at least one first environment, by the at least onespecially programmed processing unit, via at least oneelectronically-controlled device, at least one change in the at leastone first environment.
 2. The method of claim 1, wherein at least onethe remotely located recording sensor node comprises a digital imagecapture device, and wherein the environmental data being captured by theat least one the remotely located recording sensor node is related tolight images selected from the group consisting of infrared at lightimages, visible light images, ultraviolet light images, and anycombination thereof.
 3. The method of claim 1, wherein the at least onefirst occurrence comprises at least one of: i) a reference, ii) anincident, iii) an accident, iv) an event, v) a change in data sequence,vi) a change in time domain, vii) any combination thereof.
 4. The methodof claim 1, wherein each remotely located recording sensor node of theplurality of remotely located recording sensor nodes is associated withat least one location identifier.
 5. The method of claim 1, wherein theelectronically establishing, based on the at least one patternrecognition criterion, the first correspondence and the secondcorrespondence, further comprising: analyzing the environmental data byutilizing at least one of the following technique: i) a fuzzy logictechnique, ii) an artificial neural network modeling technique, iii) agenetic algorithm technique, iv) a rough set technique, v) awavelets-based technique, and vi) any combination thereof.
 6. The methodof claim 1, wherein the plurality of environmental parameters arerelated to a plurality distributed equipment pieces.
 7. The method ofclaim 1, wherein the electronically establishing, based on the at leastone pattern recognition criterion, the first correspondence and thesecond correspondence, further comprising: comparing a plurality ofinstances of the sensed data captured from the plurality of the remotelylocated recording sensor nodes over the at least one first time periodof the plurality of time periods.
 8. The method of claim 7, wherein theelectronically establishing, based on the at least one patternrecognition criterion, the first correspondence and the secondcorrespondence, further comprising: automatically assigning, by the atleast one specially programmed central processing unit, a weight to eachinstance of the plurality of instances of the sensed data.
 9. The methodof claim 8, wherein the electronically establishing, based on the atleast one pattern recognition criterion, the first correspondence andthe second correspondence, further comprising: comparing the pluralityof instances of the sensed data based on the assigned weights.
 10. Themethod of claim 1, wherein the plurality of environmental parametersfurther comprises: 3) at least one optical environmental parameter, 4)at least one acoustic environmental parameter, 5) at least one pressureenvironmental parameter, 6) at least one thermal environmentalparameter, 7) at least one acceleration environmental parameter, 8) atleast one magnetic environmental parameter, 9) at least biologicalenvironmental parameter, and 10) at least chemical environmentalparameter.
 11. The method of claim 10, wherein the at least one acousticenvironmental parameter is a sound frequency.
 12. The method of claim10, wherein the at least one acoustic environmental parameter is a soundpattern.
 13. The method of claim 10, wherein the at least one pressureenvironmental parameter is a vibration frequency.
 14. The method ofclaim 10, wherein the at least one chemical environmental parameter is apresence of airborne carbon particles.
 15. The method of claim 1,wherein each time period of the plurality of time periods is determinedbased on a pre-determined sampling rate.
 16. The method of claim 1,wherein the at least one first time period is a day.
 17. Acomputer-implemented system, comprising: a specially programmed centralprocessing unit, comprising: a non-transient memory, electronicallystoring particular computer executable program code; and at least onecomputer processor which, when executing the particular computerexecutable program code, becomes at least one specifically programmedcomputer processor of the specially programmed central processing unitthat is configured to perform at least the following operations:electronically and automatically receiving, via at least onecommunication link, sensed data from at least one sensor network;wherein the at least one sensor network comprises a plurality ofremotely located recording sensor nodes; wherein the sensed datacomprises environmental data for each environment in which each remotelylocated recording sensor node is located; wherein the environmental datais being continuously captured, over a plurality of time periods, by theplurality of remotely located recording sensor nodes, based, at least inpart, on a plurality of environmental parameters; wherein the pluralityof environmental parameters comprises: 1) at least one temperatureenvironmental parameter, and 2) at least one motion environmentalparameter; wherein each of the plurality of remotely located recordingsensor nodes captures the environmental data for at least one respectiveenvironmental parameter; automatically analyzing the sensory data,captured over at least one first time period of the plurality of timeperiods, to identify a plurality of representative features within thesensed data, wherein each representative feature of the plurality ofrepresentative features is represented by particular environmental dataof the at least one respective environmental parameter; electronicallystoring, in at least one non-transient database, data representative ofthe plurality of representative features related to the at least onefirst time period of the plurality of time periods; electronicallyestablishing, based on at least one pattern recognition criterion, afirst correspondence of at least one first representative feature fromthe plurality of representative features to at least one firstcharacteristic of at least one first occurrence and a secondcorrespondence of at least one second representative feature from theplurality of representative features to at least one secondcharacteristic of the at least one first occurrence, wherein at leastone first instance of the at least one first occurrence has occurredwithin the at least one first time period of the plurality of timeperiods; electronically discovering, based on the first correspondenceand the second correspondence, at least one second instance of the atleast one first occurrence within the sensed data captured over at leastone second time period of the plurality of time periods, wherein the atleast one first occurrence has taken place in at least one firstenvironment associated with at least one first remotely locatedrecording sensor node; and electronically causing, based on thediscovery of the at least one second instance of the at least one firstoccurrence in the at least one first environment, at least one change inthe at least one first environment via at least oneelectronically-controlled device.
 18. The system of claim 17, wherein atleast one the remotely located recording sensor node comprises a digitalimage capture device, and wherein the environmental data being capturedby the at least one the remotely located recording sensor node isrelated to light images selected from the group consisting of infraredat light images, visible light images, ultraviolet light images, and anycombination thereof.
 19. The system of claim 17, wherein the at leastone first occurrence comprises at least one of: i) a reference, ii) anincident, iii) an accident, iv) an event, v) a change in data sequence,vi) a change in time domain, vii) any combination thereof.
 20. Thesystem of claim 17, wherein each remotely located recording sensor nodeof the plurality of remotely located recording sensor nodes isassociated with at least one location identifier.
 21. The system ofclaim 17, wherein the electronically establishing, based on the at leastone pattern recognition criterion, the first correspondence and thesecond correspondence, further comprising: analyzing the environmentaldata by utilizing at least one of the following technique: i) a fuzzylogic technique, ii) an artificial neural network modeling technique,iii) a genetic algorithm technique, iv) a rough set technique, v) awavelets-based technique, and vi) any combination thereof.
 22. Thesystem of claim 17, wherein the plurality of environmental parametersare related to a plurality distributed equipment pieces.
 23. The systemof claim 17, wherein the electronically establishing, based on the atleast one pattern recognition criterion, the first correspondence andthe second correspondence, further comprising: comparing a plurality ofinstances of the sensed data captured from the plurality of the remotelylocated recording sensor nodes over the at least one first time periodof the plurality of time periods.
 24. The system of claim 23, whereinthe electronically establishing, based on the at least one patternrecognition criterion, the first correspondence and the secondcorrespondence, further comprising: automatically assigning, by the atleast one specially programmed central processing unit, a weight to eachinstance of the plurality of instances of the sensed data.
 25. Thesystem of claim 24, wherein the electronically establishing, based onthe at least one pattern recognition criterion, the first correspondenceand the second correspondence, further comprising: comparing theplurality of instances of the sensed data based on the assigned weights.26. The system of claim 17, wherein the plurality of environmentalparameters further comprises: 3) at least one optical environmentalparameter, 4) at least one acoustic environmental parameter, 5) at leastone pressure environmental parameter, 6) at least one thermalenvironmental parameter, 7) at least one acceleration environmentalparameter, 8) at least one magnetic environmental parameter, 9) at leastbiological environmental parameter, and 10) at least chemicalenvironmental parameter.
 27. The system of claim 26, wherein the atleast one acoustic environmental parameter is a sound frequency.
 28. Thesystem of claim 26, wherein the at least one chemical environmentalparameter is a presence of airborne carbon particles.
 29. The system ofclaim 17, wherein each time period of the plurality of time periods isdetermined based on a pre-determined sampling rate.
 30. The system ofclaim 17, wherein the at least one first time period is a day.